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Load packages

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.4.2
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Warning: package 'readr' was built under R version 3.4.2
## Warning: package 'purrr' was built under R version 3.4.2
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag():    dplyr, stats
library(plotly)
## Warning: package 'plotly' was built under R version 3.4.2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout

Load the data

download.file("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv", destfile = "C:\\Users\\Katrina\\Desktop\\data\\gapminder-FiveYearData.csv")
gapminder <- read.csv("C:\\Users\\Katrina\\Desktop\\data\\gapminder-FiveYearData.csv")

What does the data look like?

head(gapminder)
##       country year      pop continent lifeExp gdpPercap
## 1 Afghanistan 1952  8425333      Asia  28.801  779.4453
## 2 Afghanistan 1957  9240934      Asia  30.332  820.8530
## 3 Afghanistan 1962 10267083      Asia  31.997  853.1007
## 4 Afghanistan 1967 11537966      Asia  34.020  836.1971
## 5 Afghanistan 1972 13079460      Asia  36.088  739.9811
## 6 Afghanistan 1977 14880372      Asia  38.438  786.1134

What is the life exp per continent?

p<-ggplot(data=gapminder[gapminder$continent=="Americas",],aes(x=gdpPercap,y=lifeExp,color=continent))+
  geom_point()+
  scale_x_log10()+
  geom_smooth(method="lm")+ # adds a line per continent
  facet_wrap(~country)
p

Same plot, interactive

ggplotly(p)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Making your own functions

If you are repeating yourself in your code, you may be able to solve that problem by making your own function!

Data manipulation with dplyr

You will likely want to get subsections of your dataframe and/or calculate means of a variable for a certain subsection, dplyr is your friend!

Data manipulation with tidyr

R likes to have ‘long’ format data where every row is an observation and you have a single column for ‘observations’ the others serve to identify that observation. (exceptions apply when you have multiple types of observations) To switch back and forth from ‘wide’ (how we typically enter data in a spreadsheet) to ‘long’ use tidyr

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plot(cars)

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